Region-growing algorithm

ABSTRACT

A region growing algorithm for controlling leakage is presented including a processor configured to select a starting point for segmentation of data, initiate a propagation process by designating adjacent voxels around the starting point, determine whether any new voxels are segmented, count and analyze the segmented new voxels to determine leakage levels, and identify and record segmented new voxels from a previous iteration when the leakage levels exceed a predetermined threshold. The processor is further configured to perform labeling of the segmented new voxels of the previous iteration, select the segmented new voxels from the previous iteration when the leakage levels fall below the predetermined threshold, and create a voxel list based on acceptable segmented voxels found in the previous iteration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. application Ser. No. 13/019,261filed Feb. 1, 2011, which claims priority to U.S. ProvisionalApplication Ser. No. 61/300,423 filed Feb. 1, 2010, which are herebyincorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

Navigating the airways of the lungs has always presented challenges tophysicians attempting to diagnose and treat lesions trans-luminally. Assuch, numerous navigational aids and imaging tools have been developedand/or utilized to provide physicians a “map” of the lungs.

One such tool is a CT scanner. CT scanners use X-ray technology to takemultiple scans or “slices” of the lungs. These scans each represent across-section of the lungs and can be viewed individually or assembled,via computer programs, to form a three-dimensional CT model. However, CTscans, like most images using X-ray technology, are somewhat cloudy andtranslucent in nature and difficult to view. As such, computer graphicstechniques are employed to interpret the information provided by the CTmodel and “grow” a virtual model of the airways which mimics what mightbe seen by a bronchoscope navigating the airways. An example of thisprocess is shown and described in U.S. patent application Ser. No.11/939,537, entitled Adaptive Navigation Technique For Navigating ACatheter Through A Body Channel Or Cavity, the entirety of which isincorporated by reference herein.

This graphical technique is sometimes referred to as “region growing or3D map generation,” and presents its own challenges. For example, regiongrowing typically involves a processing of the CT data by analyzing eachtwo-dimensional pixel, or, more pertinently, three-dimensional voxel,for brightness or “density” and assigning the voxel a value thatindicates either tissue or air based on whether the density meets acertain threshold value. CT scans are grayscale images composed of aplurality of pixels (2D) or voxels (3D—if the scans have been assembledinto a volume), each pixel or voxel varying in intensity from white(most intense) to black (least intense). Each intensity level betweenwhite and black appears as a shade of gray. By designating the variousshades of gray from the CT scans either “tissue” or “air” the resultingimage of the lungs becomes much more clear. However, if the voxels aredesignated incorrectly, the model of the lungs becomes inaccurate.Incorrect voxel designation results from setting the threshold level atan incorrect value, which is an inherent problem when attempting toassign discreet values (air or tissue) to voxels which are actuallyvarious shades of gray.

A presently-used technique for optimal threshold setting is beginningwith a conservative threshold and performing a region-growing iteration.A conservative threshold is one that is not likely to result in leakage,which occurs when tissue is designated as air and creates a virtualimage of the airways that looks like air (color) is spilling out of theairways. However, even with a conservative threshold, inaccuracies inthe CT scans can result in “holes” after the segmentation process. Theseholes result in false branches.

Moreover, a conservative threshold results in airways that endprematurely. Therefore, after a conservative iteration is performed,resulting in a stunted branched structure, the threshold isincrementally increased and a second iteration is performed. These stepsare repeated until leakage occurs. Thus, the maximum threshold that doesnot result in leakage is determined and used. This approach, however,naturally results in the least-dense portion of the CT image dictatingthe threshold level. There are other problems that arise from thismethod as well.

For example, during a full inhalation, the airways stretch and thin inorder to accommodate the additional air volume. The thinning of theairways results in reduced tissue imaging density, and leakage thusarises even at lower threshold values.

Another example is that each time the threshold is increased, thealgorithm runs from the initial seed point. Hence, if the threshold isincreased ten times before leakage arises, each of the voxels analyzedin the initial iteration, is analyzed nine more times. This algorithm isthus inherently taxing on processing resources.

As such, a need is identified for a region-growing algorithm that isable to identify localized weaknesses in image data and “repair” themsuch that more distal branches of a bronchial tree can be segmented and“grown”.

SUMMARY OF THE INVENTION

The present invention provides a technique and algorithm for controllingleakage while using a high threshold during a region growing procedure.Rather than beginning with a conservative (low) threshold, a highthreshold is used but growing is analyzed and controlled.

One aspect of the present invention uses an algorithm that definesiteration as a single voxel layer of growth, rather than growth untilleakage is detected. Each of the iterations is analyzed for a predictedincrease (e.g., parabolic increase) in the number of voxels. If thevoxels are not growing according to an expected rate, the unexpectedincrease is identified as leakage.

Rather than ending the region-growing process when leakage is detected,the point of leakage is isolated or “patched” so that further iterationson the current path do not continue from that point, but the rest of themodel is allowed continued growth.

Another aspect of the present invention provides a post-iteration stepwhich, in effect, repairs or salvages branches encountering leakage.After all of the iterations are performed, branches that had leakage areretrieved from a stored location and regrown with a reduced threshold.These branches are then added to the model.

The advantages of the algorithm of the present invention are numerous.One advantage is a more meaningful increase in the number of branches asa result of the utilization of the highest possible threshold.

Another advantage of the present invention is that the appearance of“holes” in the central segmented area of the model is greatly reduced.

Yet another advantage of the present invention is a reduced number offalse branches. Because each iteration is analyzed, growth is controlledand false branches are prevented from growing.

Another advantage of the present invention is that the branches grownhave an increased length, due to an optimized threshold as well assecondary growing efforts.

Still another advantage of the present invention is reduced importanceon initial threshold value selection. Through experimentation, it hasbeen found that this algorithm is self-correcting, yielding virtuallyidentical results regardless of the initial threshold used, so long asthe thresholds are relatively high.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow-chart of a preferred embodiment of an algorithm of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

The technique 10 of the present invention is charted in the flowchartpresented as FIG. 1. The technique 10 begins at 20 by selecting astarting point for the segmentation of the CT data. For example,selecting a point in the trachea is a logical starting point as it isthe largest, and most proximal airway, and easily recognizable on a CTscan. Preferably, the starting point is a single voxel inside thetrachea that is centered and meets a high threshold value. In otherwords, a voxel which is clearly air is selected. Any point from whichfurther adjacent voxels are analyzed is hereinafter referred to as a“seed point”. The starting point is the first seed point.

At 30 the propagation process is initiated by designating adjacentvoxels around the starting point. This designation is known assegmentation, and it indicates that the new voxels met the thresholdlevel for air. Because the starting point is preferably selected in thetrachea, and it is not desired to grow the airway back toward the mouthof the patient, only voxels in a distal direction of the starting pointare segmented. As such, a “wave” is generated that travels distally intothe airways. It is understood that the branches of the lungs fan out inall directions. As such, “distal direction” as used herein isinterpreted as getting further away from the starting point along a paththat remains in the airway. In other words, some “distal” points in theairways may be relatively close to the starting point if one were to cutthrough tissue to make a straight line between the points.

It is understood that the starting point, being a single voxel, issurrounded by 26 adjacent voxels, 17 of which are extending in adirection of desired growth (in a direction not yet segmented and not ina reverse direction, such as toward the mouth).

At 40, it is determined whether any new voxels were segmented. In otherwords, if no voxels in that “wave” met the threshold level for air,there is no growth and the tree is complete.

If the answer at 40 is “yes”, the algorithm continues to 50, where thesegmented voxels are counted and analyzed to determine whether there isleakage. The new voxels may be more numerous than the previousiteration, but the growth should be controlled. In other words, if theincrease in the number of voxels is expected (more specifically, aparabolic increase has been observed in normal growth patterns withoutleakage) then it is determined that there is no leakage and the processreturns to step 30 to begin a new iteration. For example, as indicatedabove, the starting point consisted of one voxel. Assuming the startingpoint was surrounded by air, the next “wave” of voxels would beseventeen new seedpoints. However, because many of these are adjacent toeach other, the next successive wave would not give rise to seventeennew seed points for each of the previous seventeen seed points.Additionally, the voxels behind each seed point that have been alreadyanalyzed, are not segmented again. If the airway being segmented wereperfectly cylindrical, as soon as the seedpoints reached the walls ofthe airway, the “wave-front” would be a convex sheet, a single voxel inthickness, and would remain constant. Hence, the mathematical model forgrowing is somewhat parabolic, except when new branches are introduced,and considering that the airways are narrowing in the distal direction.Leakage, however, results in an abrupt increase in the number ofsegmented voxels.

If at 50 the analysis results in an unexpected or abnormally highincrease in segmented voxels, it is determined that leakage exists andthe process moves to step 60, which identifies and records the segmentedvoxels from the previous iteration and labels them as accurate.

Leakage determination is derived from two important conclusions: (1) Itis expected that the front size has an inverse (not necessarily linear)dependence on the iteration number, e.g. [front size]˜f(1/iterationnumber). (2) Bifurcations and changes in airway shape may result insomewhat linear growth in front size.

At 70, upon the detection of leakage, an analysis is done in order toselect the most recent “good” iteration that does not contain leakage.This decision is based on the same principals used to satisfy themathematical model in compliance with the natural structure of thebronchial tree.

The voxels segmented up to the last good iteration are assumed to becommitted to the segmented voxel list, while voxels that belong to eachiteration above the “good” one are analyzed in order to separate thevoxels that led to the leakage. In order to make this analysis, recentlysegmented voxels are labeled to connected objects (part of the branch).Each object is then analyzed in order to detect the object that causedleakage.

The coordinates of voxels that belong to inaccurate objects are storedseparately and treated differently thereafter. They are locked in thebinary map to prevent their participation in the segmentation process.Voxels, belonging to accurate branches are returned to the sementationand the process returns to 30.

At 80 the objects identified as leaking are removed from furthersegmentation and stored in a queue for post processing. After 80 theprocess returns to 30 for the next iteration.

If at 50 the answer was “no” for leakage, the process returns to 30 forthe next iteration. It should be noted that the flow chart 10, thoughpresented in series for clarification, is actually a parallel operation.In other words, each new voxel is a seed point and the flow chart isperformed on each next iteration therefrom simultaneously. Hence,viewing the growth of the bronchial tree real time, one would see anear-instant tree appear, depending of course on the power of theprocessor running the algorithm.

If at 40 there are no new voxels detected, the algorithm proceeds to 90,which is a decision step asking whether any leakage objects wereidentified. If the answer is “yes” step 100 is executed, which retrievesthe last object from the storage queue (step 80).

Next at 110, the threshold is reduced and the algorithm is performed ononly the selected leakage object. Because a reduced threshold is beingused, the likelihood of leakage is reduced.

If at 90 it is determined that there are no leakage objects, eitherbecause there were none or they have all been reprocessed, the processis completed at 120.

Although the invention has been described in terms of particularembodiments and applications, one of ordinary skill in the art, in lightof this teaching, can generate additional embodiments and modificationswithout departing from the spirit of or exceeding the scope of theclaimed invention. Accordingly, it is to be understood that the drawingsand descriptions herein are proffered by way of example to facilitatecomprehension of the invention and should not be construed to limit thescope thereof.

What is claimed is:
 1. A region growing algorithm for controllingleakage comprising a processor configured to: select a starting pointfor segmentation of data; initiate a propagation process by designatingadjacent voxels around the starting point; determine whether any newvoxels are segmented; count and analyze the segmented new voxels todetermine leakage levels; identify and record segmented new voxels froma previous iteration when the leakage levels exceed a predeterminedthreshold; perform labeling of the segmented new voxels of the previousiteration; select the segmented new voxels from the previous iterationwhen the leakage levels fall below the predetermined threshold; andcreate a voxel list based on acceptable segmented voxels found in theprevious iteration.
 2. The region growing algorithm of claim 1, whereinthe starting point is a point in a trachea.
 3. The region growingalgorithm of claim 2, wherein the starting point is a single voxelinside the trachea that meets a high threshold value.
 4. The regiongrowing algorithm of claim 3, wherein the single voxel is surrounded by26 adjacent voxels, 17 voxels extending in a direction of growth.
 5. Theregion growing algorithm of claim 1, wherein the data is computedtomography (CT) scan data.
 6. The region growing algorithm of claim 1,wherein voxels in a distal direction of the starting point aresegmented.
 7. The region growing algorithm of claim 1, wherein the voxellist is used to create a virtual model of an anatomical structure.
 8. Aregion growing algorithm for creating a virtual model of an anatomicalstructure, the region growing algorithm comprising a processorconfigured to: provide a three-dimensional image volume created from aplurality of two-dimensional images; set a threshold value, above whichis indicative that an imaged voxel is not representing tissue; select aseed point voxel in the three-dimensional image volume as a startingpoint; perform a plurality of first iterations; detect when a number ofvoxels segmented equals zero; and perform a plurality of seconditerations on leaked objects, if any, until the number of voxelssegmented equals zero.
 9. The region growing algorithm of claim 8,wherein each of the plurality of first iterations includes segmentingvoxels, located adjacent the seed point in a plurality of directions,that meet or exceed the threshold value.
 10. The region growingalgorithm of claim 9, wherein each of the plurality of first iterationsfurther includes determining a number of voxels segmented in thisiteration and if the number of voxels is greater than zero, determiningwhether the number is large enough to indicate leakage.
 11. The regiongrowing algorithm of claim 10, wherein each of the plurality of firstiterations further includes removing leaked voxels and storing theleaked voxels as leaked objects for post-processing.
 12. The regiongrowing algorithm of claim 11, wherein each of the plurality of firstiterations further includes identifying segmented voxels as seed pointsfor a next iteration if no leakage is indicated.
 13. The region growingalgorithm of claim 8, wherein each of the plurality of second iterationsinclude selecting voxels in a first iteration detecting leakage for agiven leaked object as seed points.
 14. The region growing algorithm ofclaim 13, wherein the plurality of second iterations further includereducing the threshold value.
 15. The region growing algorithm of claim14, wherein the plurality of second iterations further includesegmenting voxels, located adjacent the seed points in a plurality ofdirections, that meet or exceed the threshold value.
 16. The regiongrowing algorithm of claim 15, wherein the plurality of seconditerations further include determining a number of voxels segmented inthis iteration and if the number of voxels is greater than zero,determining whether the number is large enough to indicate leakage. 17.The region growing algorithm of claim 16, wherein the plurality ofsecond iterations further include removing leaked voxels and storing theleaked voxels as leaked objects for post-processing.
 18. The regiongrowing algorithm of claim 17, wherein the plurality of seconditerations further include identifying segmented voxels as seed pointsfor a next iteration if no leakage is indicated.
 19. A region growingalgorithm for creating a virtual model of an anatomical structure from athree-dimensional image volume, the region growing algorithm comprisinga processor configured to: use, as a threshold, an image voxel densityindicative of a fluid; segment voxels meeting the threshold; create avirtual model of interior cavities or lumens of the anatomicalstructure; perform a series of iterations using a segmented voxel as aseed point; analyze non-segmented voxels adjacent the seed point in aplurality of directions; repeat the iterations using newly segmentedvoxels from an iteration as seed points for a next iteration until nonon-segmented voxels adjacent the seed points in the plurality ofdirections remain; and separate voxels meeting the threshold but formingleaked objects that fail to satisfy one or more criteria based oncomporting to a known anatomical structure.
 20. The region growingalgorithm of claim 19, wherein the processor further re-segments theleaked objects using a reduced threshold and separates voxels meetingthe reduced threshold but forming the leaked objects.